The study introduces the Botanical Spectrum Analyzer (BSA), a GUI that incorporates a modified U‑Net deep neural network for accurate segmentation of plant images from RGB and hyperspectral (VNIR and SWIR) data. BSA was tested on wheat, barley, and Arabidopsis datasets, achieving >99% accuracy and F1‑scores above 98%, and markedly outperformed commercial tools on root segmentation tasks.
The study shows that the membrane lipids PI4P, PI(4,5)P2, and phosphatidylserine have distinct spatial and temporal dynamics during lateral root primordium formation in Arabidopsis thaliana, with PI4P acting as a stable basal lipid, PI(4,5)P2 serving as a negative regulator of initiation, and phosphatidylserine increasing after founder cell activation. Using live-cell biosensors, genetic mutants, and an inducible PI(4,5)P2 depletion system, the authors demonstrate that reducing PI(4,5)P2 enhances lateral root initiation and development.
Phenotypic scoring of Canola Blackleg severity using machine learning image analysis
Authors: Hu, Q., Anderson, S. N., Gardner, S., Ernst, T. W., Koscielny, C. B., Bahia, N. S., Johnson, C. G., Jarvis, A. C., Hynek, J., Coles, N., Falak, I., Charne, D. R., Ruidiaz, M. E., Linares, J. N., Mazis, A., Stanton, D. J.
The study introduces a deep‑learning based image analysis pipeline that scores blackleg disease severity from stem cross‑section images of canola species, achieving greater consistency than median expert raters while preserving comparable heritability of susceptibility traits. This standardized scoring method aims to improve selection of resistant varieties in breeding programs.
The study presents a deep‑learning pipeline that uses state‑of‑the‑art convolutional neural networks to automatically estimate the establishment of perennial groundcovers in agricultural research plots from smartphone images. By employing region‑of‑interest markers and deploying the models on AWS SageMaker with a lightweight Django web interface, the approach provides fast, objective, and reproducible assessments that can be adopted by researchers and growers across the Midwest.
The study engineers Type‑B response regulators to alter their transcriptional activity and cytokinin sensitivity, enabling precise modulation of cytokinin‑dependent traits. Using tissue‑specific promoters, the synthetic transcription factors were deployed in Arabidopsis thaliana to reliably increase or decrease lateral root numbers, demonstrating a modular platform for controlling developmental phenotypes.
Unraveling the cis-regulatory code controlling abscisic acid-dependent gene expression in Arabidopsis using deep learning
Authors: Opdebeeck, H., Smet, D., Thierens, S., Minne, M., De Beukelaer, H., Zuallaert, J., Van Bel, M., Van Montagu, M., Degroeve, S., De Rybel, B., Vandepoele, K.
The study used an interpretable convolutional neural network to predict ABA responsiveness from proximal promoter sequences in Arabidopsis thaliana, revealing both known ABF-binding motifs and novel regulatory elements. Model performance was boosted by advanced data augmentation, and predicted regulatory regions were experimentally validated using reporter lines, confirming the inferred cis‑regulatory code.
The study examines how the SnRK1 catalytic subunit KIN10 integrates carbon availability with root growth regulation in Arabidopsis thaliana. Loss of KIN10 reduces glucose‑induced inhibition of root elongation and triggers widespread transcriptional reprogramming of metabolic and hormonal pathways, notably affecting auxin and jasmonate signaling under sucrose supplementation. These findings highlight KIN10 as a central hub linking energy status to developmental and environmental cues in roots.
The study presents GenoRetriever, an interpretable deep learning framework trained on STRIPE-seq data from soybean and other crops, that predicts transcription start site locations and usage by identifying 27 core promoter motifs. Validation using in silico motif insertions, saturation mutagenesis, and CRISPR‑Cas9 promoter editing demonstrates high predictive accuracy and reveals domestication‑driven motif usage shifts and lineage‑specific effects. The tool is provided via a web server for promoter analysis and design, offering a new resource for plant functional genomics and crop improvement.
The study characterizes the tomato class B heat shock factor SlHSFB3a, revealing its age‑dependent expression in roots and its role in enhancing lateral root density by modulating auxin homeostasis. Overexpression of SlHSFB3a increases lateral root emergence, while CRISPR‑mediated knockouts produce the opposite phenotype, indicating that SlHSFB3a regulates auxin signaling through repression of auxin repressors and activation of the ARF7/LOB20 pathway.
A forward genetic screen in light-grown Arabidopsis seedlings identified the Evening Complex component ELF3 as a key inhibitor of phototropic hypocotyl bending under high red:far-red and blue light, acting upstream of PIF4/PIF5. ELF3 and its partner LUX also mediate circadian regulation of phototropism, and the orthologous ELF3 in Brachypodium distachyon influences phototropism in the opposite direction.